The existing definitions of visual impairment in the International Statistical Classification of Diseases are based on recommendations made over 30 years ago. New data and knowledge related to visual impairment that have accumulated over this period suggest that these definitions need to be revised.

In this cohort study of hospitalized patients with linked medical record data, we developed International Classification of Diseases (ICD) criteria that accurately identified laboratory-confirmed, severe influenza hospitalizations (positive predictive value [PPV] 80%, 95% confidence interval [CI] 71-87%), which we validated through medical record documentation. These criteria identify patients with clinically important influenza illness outcomes to inform evaluation of preventive and therapeutic interventions and public health policy recommendations.

International Classification of Diseases (ICD) codes have been used to ascertain individuals who are obese. There has been limited research about the predictive value of ICD-coded obesity for major chronic conditions at the population level. We tested the utility of ICD-coded obesity versus measured obesity for predicting incident major osteoporotic fracture (MOF), after adjusting for covariates (i.e., age and sex). In this historical cohort study (2001-2015), we selected 61,854 individuals aged 50 years and older from the Manitoba Bone Mineral Density Database, Canada. Body mass index (BMI) ≥30 kg/m2 was used to define measured obesity. Hospital and physician ICD codes were used to ascertain ICD-coded obesity and incident MOF. Average cohort age was 66.3 years and 90.3% were female. The sensitivity, specificity and positive predictive value for ICD-coded obesity using measured obesity as the reference were 0.11 (95% confidence interval [CI]: 0.10, 0.11), 0.99 (95% CI: 0.99, 0.99) and 0.79 (95% CI: 0.77, 0.81), respectively. ICD-coded obesity (adjusted hazard ratio [HR] 0.83; 95% CI: 0.70, 0.99) and measured obesity (adjusted HR 0.83; 95% CI: 0.78, 0.88) were associated with decreased MOF risk. Although the area under the receiver operating characteristic curve (AUROC) estimates for incident MOF were not significantly different for ICD-coded obesity versus measured obesity (0.648 for ICD-coded obesity versus 0.650 for measured obesity; P = 0.056 for AUROC difference), the category-free net reclassification index for ICD-coded obesity versus measured obesity was -0.08 (95% CI: -0.11, -0.06) for predicting incident MOF. ICD-coded obesity predicted incident MOF, though it had low sensitivity and reclassified MOF risk slightly less well than measured obesity.

Rare diseases present a wide spectrum of clinical manifestations and severity levels and are often poorly known and underrepresented, making them difficult to classify. Diagnoses are usually coded using the International Classification of Diseases (ICD), with its different versions. In Spain, the ICD-10-ES (stem from the ICD-10-CM-Clinical Modification) is used throughout the National Healthcare System since 2016, indistinctively including rare diseases that often lack a specific code. Orphanet aims to provide high-quality resources on rare diseases. The goal was to interrelate the Orphanet classification with the ICD-10-ES in order to engage a tool to track rare diseases diagnosis and characterize the improvement space for the identification of rare diseases patients in the Spanish Healthcare System.

The World Health Organization launched the International Classification of Diseases for Perinatal Mortality (ICD-PM) in 2016 to uniformly report on the causes of perinatal deaths. In this systematic review, we aim to describe the global use of the ICD-PM by reporting causes of perinatal mortality and summarizing challenges and suggested amendments.

Although currently misclassified in the International Classification of Diseases (ICD) and still not officially listed as a rare disease, anaphylaxis is a well-known clinical emergency. Anaphylaxis is now one of the principal headings in the "Allergic and hypersensitivity conditions" section recently compiled for the forthcoming 11th Revision of ICD (ICD-11). We here report the building process used for the pioneering "Anaphylaxis" subsection of ICD-11 in which we aimed for transparency as recommended in the ICD-11 revision guidelines.

The automatic coding of clinical text documents by using the International Classification of Diseases, 10th Revision (ICD-10) can be performed for statistical analyses and reimbursements. With the development of natural language processing models, new transformer architectures with attention mechanisms have outperformed previous models. Although multicenter training may increase a model's performance and external validity, the privacy of clinical documents should be protected. We used federated learning to train a model with multicenter data, without sharing data per se.

Intentional and unintentional injuries are a leading cause of death and disability globally. International Classification of Diseases (ICD), Tenth Revision (ICD-10) codes are used to classify injuries in administrative health data and are widely used for health care planning and delivery, research, and policy. However, a systematic review of their overall validity and reliability has not yet been done.

To evaluate the impact of International Classification of Disease, 10th revision, Clinical Modification (ICD-10-CM) implementation on pneumonia hospitalizations rates, which had declined following pneumococcal conjugate vaccine introduction for infants in 2000.

Administrative databases are increasingly used in research studies to capture clinical outcomes such as sepsis. This systematic review and meta-analysis examines the accuracy of International Classification of Diseases, 10th revision (ICD-10), codes for identifying sepsis in adult and pediatric patients.

The 10th and 9th revisions of the International Statistical Classification of Diseases and Related Health Problems (ICD10 and ICD9) have been adopted worldwide as a well-recognized norm to share codes for diseases, signs and symptoms, abnormal findings, etc. The international Consortium for Clinical Characterization of COVID-19 by EHR (4CE) website stores diagnosis COVID-19 disease data using ICD10 and ICD9 codes. However, the ICD systems are difficult to decode due to their many shortcomings, which can be addressed using ontology.

The real prevalence and incidence of women living with or at risk of female genital mutilation/cutting (FGM/C) is unknown in Switzerland and many parts of Europe, as there are no representative surveys similar to DHS or MICS for European countries. Indirect estimates are commonly used to estimate the number of women with FGM/C in high-income countries, but may not reflect the actual FGM/C prevalence among migrants. Direct measures may provide more accurate estimates that could guide policy- and clinical decision-making. Swiss hospital data may provide a sample of patients that can be used to describe the prevalence of FGM/C in Swiss hospitals. Our study assesses the number of inpatient women and girls in Swiss university hospitals from countries with high FGM/C prevalence, and of inpatients with a coded diagnosis of FGM/C.

Trauma is the cause of 1.2 million deaths in India annually. Injury severity scores play an important role in trauma research and care because these scores enable the adjustment of trauma severity when comparing mortality outcomes. The generalizability of the International Classification of Diseases Injury Severity Score (ICISS) between different populations is not fully known, and the validity of the ICISS has not been assessed in the Indian context. The aim of this study was to assess the predictive performances of three international versions of the ICISS, derived from data from Australia, New Zealand and pooled data from seven different high-income countries, in trauma patients admitted to four public hospitals in urban India.

We report the updated classification of primary immunodeficiency diseases, compiled by the ad hoc Expert Committee of the International Union of Immunological Societies. As compared to the previous edition, more than 15 novel disease entities have been added in the updated version. For each disorders, the key clinical and laboratory features are provided. This updated classification is meant to help in the diagnostic approach to patients with these diseases.

No abstract available

Hospital discharge codes are increasingly used in gastroenterology research, but their accuracy in the setting of acute pancreatitis (AP) and chronic pancreatitis (CP), one of the most frequent digestive diseases, has never been assessed systematically. The aim was to conduct a systematic literature review and determine accuracy of diagnostic codes for AP and CP, as well as the effect of covariates.

NONE DECLARED.

International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses.

This paper investigated the use of language models and deep learning techniques for automating disease prediction from symptoms. Specifically, we explored the use of two Medical Concept Normalization-Bidirectional Encoder Representations from Transformers (MCN-BERT) models and a Bidirectional Long Short-Term Memory (BiLSTM) model, each optimized with a different hyperparameter optimization method, to predict diseases from symptom descriptions. In this paper, we utilized two distinct dataset called Dataset-1, and Dataset-2. Dataset-1 consists of 1,200 data points, with each point representing a unique combination of disease labels and symptom descriptions. While, Dataset-2 is designed to identify Adverse Drug Reactions (ADRs) from Twitter data, comprising 23,516 rows categorized as ADR (1) or Non-ADR (0) tweets. The results indicate that the MCN-BERT model optimized with AdamP achieved 99.58% accuracy for Dataset-1 and 96.15% accuracy for Dataset-2. The MCN-BERT model optimized with AdamW performed well with 98.33% accuracy for Dataset-1 and 95.15% for Dataset-2, while the BiLSTM model optimized with Hyperopt achieved 97.08% accuracy for Dataset-1 and 94.15% for Dataset-2. Our findings suggest that language models and deep learning techniques have promise for supporting earlier detection and more prompt treatment of diseases, as well as expanding remote diagnostic capabilities. The MCN-BERT and BiLSTM models demonstrated robust performance in accurately predicting diseases from symptoms, indicating the potential for further related research.

The decision-making in clinical nursing, regarding diagnoses, interventions and outcomes, can be assessed using standardized language systems such as NANDA International, the Nursing Interventions Classification and the Nursing Outcome Classification; these taxonomies are the most commonly used by nurses in informatized clinical records. The purpose of this review is to synthesize the evidence on the effectiveness of the nursing process with standardized terminology using the NANDA International, the Nursing Interventions Classification and the Nursing Outcome Classification in care practice to assess the association between the presence of the related/risk factors and the clinical decision-making about nursing diagnosis, assessing the effectiveness of nursing interventions and health outcomes, and increasing people's satisfaction. A systematic review was carried out in Medline and PreMedline (OvidSP), Embase (Embase-Elsevier), The Cochrane Library (Wiley), CINAHL (EbscoHOST), SCI-EXPANDED, SSCI and Scielo (WOS), LILACS (Health Virtual Library) and SCOPUS (SCOPUS-Elsevier) and included randomized clinical trials as well as quasi-experimental, cohort and case-control studies. Selection and critical appraisal were conducted by two independent reviewers. The certainty of the evidence was assessed with the Grading of Recommendations Assessment, Development and Evaluation Methodology. A total of 17 studies were included with variability in the level and certainty of evidence. According to the outcomes, 6 studies assessed diagnostic decision-making and 11 assessed improvements in individual health outcomes. No studies assessed improvements in intervention effectiveness or population satisfaction. There is a need to increase studies with rigorous methodologies that address clinical decision-making about nursing diagnoses using NANDA International and individuals' health outcomes using the Nursing Interventions Classification and the Nursing Outcome Classification as well as implementing studies that assess the use of these terminologies for improvements in the effectiveness of nurses' interventions and population satisfaction with the nursing process.